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Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection.

Robert J Harris1, Scott G Baginski2, Yulia Bronstein2

  • 1Virtual Radiologic, Eden Prairie, MN, USA. robert.harris@vrad.com.

Journal of Digital Imaging
|July 29, 2021
PubMed
Summary
This summary is machine-generated.

A machine-learning model accurately measures endotracheal tube (ET) tip to carina distance on chest X-rays (CXRs). This AI tool performs comparably to human radiologists, aiding in clinical reporting and prioritizing patient care.

Keywords:
Bounding boxChest X-rayConvolutional neural networkEndotracheal tubeMachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Endotracheal tube (ETT) placement confirmation via chest X-ray (CXR) is standard practice.
  • Radiologists measure the distance between the ETT tip and carina to assess positioning.
  • Inter-observer variability exists in manual ETT tip to carina measurements.

Purpose of the Study:

  • To develop and validate a machine-learning (ML) model for automated ETT tip to carina distance measurement on CXRs.
  • To compare the ML model's accuracy against human radiologist measurements and inter-radiologist variability.

Main Methods:

  • A large dataset of CXRs with annotated ETT and carina bounding boxes was used to train an ML model.
  • The ML model was evaluated on a gold standard dataset and prospective clinical data.
  • Inter-radiologist variability was assessed on a separate test dataset.

Main Results:

  • The ML model achieved a mean error of 0.70 cm on the gold standard dataset and 0.68 cm on prospective data.
  • The model's performance was non-inferior to inter-radiologist variability (mean error 0.70 cm) within a 0.1 cm equivalence bound.
  • The model demonstrated accuracy comparable to human measurements.

Conclusions:

  • The developed ML model provides accurate and reliable ETT tip to carina distance measurements on CXRs.
  • The model's performance is equivalent to human radiologists, supporting its clinical utility.
  • Automated measurements can enhance clinical reporting and prioritize cases with malpositioned tubes.